ETC5521 Assignment 1
Analysis of global mobile and fixed telephone usage
Hanchen Wang
Jiaying Zhang
2021-08-26
This assignment is for ETC5521 Assignment 1 by Team brolga comprising of Hanchen Wang and Jiaying Zhang.
1 Introduction and motivation
It has been 45 years since the first landline telephone was invented in 1876. The telephone has changed a lot from how it works to how it looks. Today, mobile technology has spread rapidly around the globe and it is estimated that more than 5 billion people have mobile devices, and over half of these connections are smartphones.
“Digital connectivity plays a critical role in bettering lives, as it opens the door to unprecedented knowledge, employment and financial opportunities for billions of people worldwide,” said ITU Secretary-General Houlin Zhao. Because the telephone plays a crucial role in our life, we think it is valuable to analyze it.
This analysis is based on information about mobile and landline phones collected by the ITU (2021) -International Telecommunication Union and would like to analyze the phone subscription trend, percentage and other factors.
Specially:
1.What is the trend in subscription of phone and landline worldwide between 1990 and 2017?
2.What was the growth rate of the subscription of mobile phones in each country from 2013 to 2017 and what was the proportion of the subscription of phone and landline from 2000?
3.What is the regression relationship between phone subscriptions and other variables?
2 Data description
The datasets are downloaded from the Github repository of Tidy Tuesday. Tidy Tuesday (2021) is a weekly data project aimed at the R ecosystem and this report will use datasets adopted by it on November 10, 2020.
There are two datasets provided in the repository and all of them came from an article-“Technology Adoption” which written by Ritchie and Roser (2017) on 2017 OurWorldInData.org.
Tidy Tuesday (2021) only made a few changes to the original data and already can get relatively clean data that can be used for analysis, mainly by matching the time of several original data, screening the data from 1990 to 2017, and adding the corresponding continent of each country.
2.1 Fixed(landline) telephone subscriptions vs GDP per capita (landline.csv)
The dataset about Fixed(landline) telephone subscriptions vs GDP per capita also originated from “Technology Adoption.” It published by (Worldbank 2021 – World Development Indicators)(http://data.worldbank.org/data-catalog/world-development-indicators) and collected by (ITU 2021 - International Telecommunication Union)(https://www.itu.int/en/ITU-D/Statistics/Pages/publications/wtid.aspx). The dataset cover the data about fixed telephone subscriptions and GPD per capita in each country during 1960 and 2017.
Fixed telephone subscriptions refers to the sum of active number of analogue fixed telephone lines, voice-over-IP (VoIP) subscriptions, fixed wireless local loop (WLL) subscriptions, ISDN voice-channel equivalents and fixed public payphones.
2.1.1 Structure of landline.csv
This dataset has 6974 number of observations and 7 number of variables. The name, type and description of each variable in landline.csv can be found in the data dictionary below.
| variable | class | description |
|---|---|---|
| entity | character | Country |
| code | character | Country code |
| year | double | Year |
| total_pop | double | Gapminder total population |
| gdp_per_cap | double | GDP per capita, PPP (constant 2011 international $) |
| landline_subs | double | Fixed telephone subscriptions (per 100 people) |
| continent | character | Continent |
2.1.2 Collection methods
Data on fixed telephone lines are derived using administrative data that countries (usually the regulatory telecommunication authority or the Ministry in charge of telecommunications) regularly, and at least annually, collect from telecommunications operators.
Data for this indicator are readily available for approximately 90 percent of countries, either through ITU’s World Telecommunication Indicators questionnaires or from official information available on the Ministry or Regulator’s website. For the rest, information can be aggregated through operators’ data (mainly through annual reports) and complemented by market research reports.
2.1.3 Data Limitation
- Discrepancies between global and national figures may arise when countries use a different definition than the one used by ITU.Data are usually not adjusted but discrepancies in the definition, reference year or the break in comparability in between years are noted in a data note. For this reason, data are not always strictly comparable. Missing values are estimated by ITU.
Figure 2.1: Visualise the missing value in landline data
- From Figure 2.1, we can see that there are 4 variables that have missing values. Among them, there are many null values in
gdp_per_capandlandline_subs, which may have come influence on the results of statistical analysis. - This data only covers the period from 1990 to 2017. If researchers want to keep up to date with the last few years, they need to look at other data as well.
2.2 Mobilephone subscriptions vs GDP per capita (mobile.csv)
The dataset about Mobilephone subscriptions vs GDP per capita originated from an article-“Technology Adoption.” This dataset was published by (Worldbank 2021 – World Development Indicators)(http://data.worldbank.org/data-catalog/world-development-indicators) and collected by (ITU 2021 - International Telecommunication Union)(https://www.itu.int/en/ITU-D/Statistics/Pages/publications/wtid.aspx). The dataset cover the data about mobilephone subscriptions and GPD per capita in each country from 1960 to 2017.
2.2.1 Structure of mobile.csv
This dataset has 6277 number of observations and 7 number of variables. The name, type and description of each variable in mobile.csv can be found in the data dictionary below.
| variable | class | description |
|---|---|---|
| entity | character | Country |
| code | character | Country code |
| year | double | Year |
| total_pop | double | Gapminder total population |
| gdp_per_cap | double | GDP per capita, PPP (constant 2011 international $) |
| mobile_subs | double | Fixed mobile subscriptions (per 100 people) |
| continent | character | Continent |
Mobile cellular telephone subscriptions are subscriptions to a public mobile telephone service that provide access to the PSTN using cellular technology. The indicator includes (and is split into) the number of postpaid subscriptions, and the number of active prepaid accounts (i.e. that have been used during the last three months). The indicator applies to all mobile cellular subscriptions that offer voice communications. It excludes subscriptions via data cards or USB modems, subscriptions to public mobile data services, private trunked mobile radio, telepoint, radio paging and telemetry services.
2.2.2 Collection methods
Data on mobile cellular subscribers are derived using administrative data that countries (usually the regulatory telecommunication authority or the Ministry in charge of telecommunications) regularly, and at least annually, collect from telecommunications operators.
Data for this indicator are readily available for approximately 90 percent of countries, either through ITU’s World Telecommunication Indicators questionnaires or from official information available on the Ministry or Regulator’s website. For the rest, information can be aggregated through operators’ data (mainly through annual reports) and complemented by market research reports.
2.2.3 Data Limitation
- Discrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. Data are usually not adjusted but discrepancies in the definition, reference year or the break in comparability in between years are noted in a data note. For this reason, data are not always strictly comparable. Missing values are estimated by ITU.
Figure 2.2: Visualise the missing value in mobile data
- From Figure 2.2, we can see that there are 4 variables that have missing values. Among them, there are many null values in
gdp_per_cap,total_popandmocile_subs, which may have some influence on the results of statistical analysis.
- This data only covers the period from 1990 to 2017. If researchers want to keep up to date with the last few years, they need to look at other data as well.
3 Analysis and findings
3.1 What is the trend in subscription of phone and landline worldwide between 1990 and 2017?
3.2 What was the growth rate of the subscription of mobile phones in each country from 2013 to 2017 and what was the proportion of the subscription of phone and landline from 2000?
3.2.1 What was the growth rate of the subscription of mobile phones in each country from 2013 to 2017?
## [1] "transparent" "transparent"Figure 3.1: Growth rate of the mobile subscription in each country during 2013 and 2017
(Put mouse on each country you can see the detail about country names and the mobilephone subscription 3 years growthrate of them.)
From the figure 3.1 we can see the increase of mobile phone subscriptions in recent years. In the three years from 2015 to 2017, the number of mobile phone subscriptions was not very large on average, staying at around 0.
There are even countries like France, Russia and Mexico that have negative growth rate. In three years the subscription of mobile phone in Libreria, decreased by 44%, which is extremely fast. However, it’s worth noting that the number of mobile subscriptions in Somania and Burma grew by 73% over the same period.
Instead of seeing a rise in the number of mobile phones in developed countries, we see a partial decline. Perhaps as mentioned by Laura Silver (2021), the developed economies have already completed the popularization of mobile phones in the early stage. Now, compared with the increase in the number, the main trend is that smart phones replace ordinary mobile phones.
3.2.2 What was the proportion of the subscription of phone and landline from 2000?
Figure 3.2: Compare proportion of the subscription of phone and landline from 2000 to 2017
As we can see from the animation 3.2, the main trend since 2000 is that the number of fixed-line phones has been decreasing while the number of mobile phones has been increasing.
European countries have more fixed-line phones, and more people still use them recently. Most African countries, by contrast, rarely use landlines.
This may be because people in European countries used to have landlines, so even as mobile phones became more popular, they still had a large proportion of people using them. However, African countries rarely used landline telephone from the beginning, so with the advent of mobile phone, they did not have the process of landline to mobile phone, and chose mobile phone directly.
Figure 3.3: Proportion of the subscription of phone and landline in 2000 and 2017
And picture 3.3 showed us more detail information, compares the 2000 and 2017 of fixed telephone and mobile phone subscription per 100 people. We can see that in most countries the number of fixed phone basically fell sharply, only less than 50, on the contrary there are quite a few people have more mobile phone at the same time. On average, most people own a mobile phone, and 100 people have an average of less than 25 landline phones.
And this trend did not happened recently, in 2006 “Cell Phone Users Are Giving up Their Landline Service” (2006) already said that one in five consumers who currently use wireless telephone service plan to drop their landline service, according to In-Stat. Besides, it’s worth noting that Monaco has a far higher percentage of fixed-line phones than any other country, while Hong Kong has nearly 2.5 mobile phones per person.
3.3 What is the regression relationship between phone subscriptions and other variables?
4 Acknowledge
- The following packages are used to produce this report: naniar (Tierney et al. 2020), dplyr (Wickham et al. 2021), readr(Wickham and Hester 2020), tidyverse (Wickham 2021), rgdal (Bivand, Keitt, and Rowlingson 2021), knitr (Xie 2021b), leaflet (Cheng, Karambelkar, and Xie 2021), ColorBrewer (Neuwirth 2014), ggplot2 (Wickham 2016), gganimate (Pedersen and Robinson 2020), gifski (Ooms 2021), plotly (Sievert 2020), bookdown(Xie 2021a)
- The background map information came from Bjorn Sandvik (2021).